As the strategic alignment of business with information technology (IT) has become more critical to the success of the business, the management of information has become a higher priority. Additionally, the volume of information that a business must manage has become increasingly large. Some of this information is critical to the business while other information has only minor value to the business. Most of the information maintained by a business falls between these two extremes.
As the volume of information increases the actual cost to maintain that information also increases. The cost of maintaining a storage infrastructure is often the largest part of an IT budget. Businesses looking to limit resources applied to the storage infrastructure must reduce the amount of information that must be stored. Furthermore, information management activities such as backup strategies, archiving strategies and storage priorities require a determination of what information to save and for how long to retain the information before it is discarded. Current methods of classifying information are commonly unstructured evaluations performed by ad hoc groups within an IT organization.
Information classification methodologies must be reproducible and produce consistent results. Furthermore, both objective and subjective factors must be considered when determining the importance of a particular piece of information to the business. Current ad hoc methodologies provide little reproducibility and fail to yield consistent results.
It is therefore desirable to provide a method and system for classifying information that overcomes the limitations, challenges, and obstacles described above.